Supply Chain Analytics:
Why it matters:
Volatility, changing demand forecasts, and supplier-specific challenges have affected nearly every organization—including those with the leading managed supply chains in the world. Even top supply chain performers have faced embarrassing stock-outs during periods of unanticipated demand in recent years. A big reason for this kind of underperformance is the fact that supply chain visibility and analytical models are typically grounded in hindsight. Making decisions based only on what happened in the past no longer provides competitive advantage
Insights that make a difference
- Use historical enterprise data to feed predictive models that support more informed decisions
- Identify hidden inefficiencies to capture greater cost savings
- Use risk modeling to conduct “pre-mortems” around significant investments and decisions
- Link supply chain models to customer and pricing analytics to clarify the whole profitability picture, not just the parts and pieces.
What to do now:
a. Treasure hunts
Leaders in supply chain performance often use “treasure hunts” to mine data for hidden opportunities. But before you start down that path, you may need to do a bit of data silo busting. That means making sure the information required to drive analytics insights is accessible.
b. Make more connections
Focusing on any single link in the supply chain will not deliver the value you’re looking for. High performance requires connecting supply chain forecasting and modeling tools to distribution models, pricing models, and even tax strategies. Only then can you dive deep into specific improvement opportunities such as promotion planning, inventory management, and channel management. The more specific the better.
Areas of focus
Forward Logistics Operations Analytics
Reverse Logistics Operations Analytics
Cycle times can be an important measure of reverse logistics. The more standardized and streamlined the processes are, the shorter the cycle time should be.
What percentage of product that moves to the reverse statistics system is reclaimed and resold? How much value is recaptured?
This metric tracks the percentage of product in the reverse logistics stream that is remanufactured/ refurbished in an appropriate manner.
How much product and other materials are moved to landfills, incinerated, or disposed of as waste? The objective is to minimize product in the waste streams.
Is the firm maximizing the profitability of product that did not sell well or has been returned by consumers?
A cost-per-touch type of metric can be readily computed by dividing total facility costs per month by the number of items processed. This is also a valuable way to compare the efficiencies of different facilities.
Tracking average distance traveled per item is not nearly as simple as determining per-item-handling cost. Generally speaking, the fewer miles that can be put on an item in the reverse logistics network, the better.
What is the total cost of ownership related to originally acquiring the product, reselling it, bringing it back as a return, and moving it through a secondary market or placing it in a landfill?
Product & Process Quality Analytics
To provide early visibility to quality trends and issues with KPIs, dashboard, alerts via desktop or mobile apps, including traceability of defects back to source – suppliers, plants, work centers, production runs, etc.
From diverse data sources – both structured and unstructured.
Supply chain analytics offers the capability to enable increased automation efficiencies and business intelligence for facilitating more proactive, effective decision making. Businesses can in turn leverage greater economies of scale while more effectively serving the unique and localized market needs of customers. Analytics insights can help positively impact top and bottom-line business growth specifically through improvements across the whole supply chain:
Make better informed, more cost-effective design decisions with time/cost analytics and simulation.
Prevent overstocking and understocking, achieve higher sales and increase customer satisfaction using demand sensing and forecasting techniques.
Optimize procurement and reduce costs using sourcing analytics for commodity pricing, risk management, spend, supplier performance management and total cost of ownership.
Optimize operations, improve process quality and prevent breakdowns with predictive analytics. Streamline & Optimize network flows, reduce costs, maximize value and improve flexibility.
Improve customer service and loyalty with IoT, machine learning and predictive analytics, detection of quality issues via warranty claims analysis, and service network and resource optimization.